Search

• Industrial robots can significantly promote firm productivity. • The main mechanism comes from the improvement of human capital structure and innovation of firms. • Industrial robots are more likely to increase the total factor productivity of non-state owned and small-scale firm. • Industrial robots are more likely to increase the total factor productivity in firms that have political affiliations and low market concentration. • Industrial robots are more likely to increase the total factor productivity in firms that low financialization, and high managerial competence. Industrial robotics has become a driving force in the development of high-quality manufacturing enterprises. This paper investigates the impact of industrial robotics on the total factor productivity of enterprises and its mechanism of action through the use of data from A-shares of Chinese companies listed on the Shanghai and Shenzhen exchanges from 2007 to 2019. We found that the use of industrial robots significantly increases the total factor productivity of enterprises, findings that still hold when Bartik instrumental variables are constructed. The mechanism of action suggests that industrial robot application improves both the human capital structure and the innovation capability of enterprises, thus increasing total factor productivity. Heterogeneity analysis shows that industrial robot applications are more likely to increase total factor productivity in firms that are non-state-owned and small scale and have political affiliations, low market concentration, low financialization, and high managerial competence. The findings of this study will help to accurately assess the productivity effects of industrial robotics, and the government should continue to promote the integration of industrial robotics and manufacturing development and actively cultivate independent innovation capabilities and professional skills in the field of industrial robotics in order to increase total factor productivity in enterprises. [ABSTRACT FROM AUTHOR]

Corporate innovation legitimacy is the key guarantee for listed companies to obtain returns from innovation activities. The purpose of this paper is to investigate the effect of analyst coverage on corporate innovation legitimacy based on effective supervision hypothesis and market pressure hypothesis. Moreover, it examines the moderating effect of analyst coverage characteristics, individual corporate behavior, external governance mechanism on the relationship between analyst coverage and corporate innovation legitimacy. The hypotheses are tested using a data set of Shanghai and Shenzhen A-share listed companies from China Research Data Service Platform (CNRDS) database, Hexun.com and CSMAR database. To overcome sample selection bias and reverse causality, this paper conduct robustness test using propensity score matching, lag variable, substitution variable and sample reduction to ensure the reliability of the research conclusions. Findings show that analyst coverage can effectively improve the innovation legitimacy of listed companies. In addition, based on the heterogeneity of the above relationship in different contexts, further research finds that the depth of analyst tracking, and the assessment of analyst optimism strengthen the positive impact of analyst coverage on innovation legitimacy. The implementation of green social responsibility brings a good reputation for the company, thereby helping analysts to focus on improving the legitimacy of innovation; In the market environment with relatively perfect external governance mechanism, the stronger the effect of analyst coverage on corporate innovation legitimacy. Prior studies mostly considered the R&D investment and other factors to overlook how other dimensions influence companies from the perspective of quantity. The research conclusion confirms the significance of analyst coverage as an external corporate governance mechanism, further enriches the theoretical research framework of innovation legitimacy, and has certain reference value for the improvement of innovation quality of listed companies. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

Timely and reliable land-use/land-cover (LULC) change dynamic monitoring is the basis of urban understanding and planning. However, either the training sample shortage or the error accumulation in the multi-temporal processing inevitably restricts the monitoring performance. In this paper, to overcome these problems, we present a label-noise robust active learning method, which automatically collects reliable and informative samples from the images and builds a unified classification system with these augmented samples. In more detail, a Bayesian sample collection process that fuses the unsupervised transition information and the multi-temporal land-cover information is designed to provide candidate samples with "from-to" labels. A reliability-based multi-classifier active learning method is then proposed to adaptively allocate the more reliable samples to the classes that are difficulty to classify. Finally, a fusion of the multiple multi-date classifications trained by the selected samples is implemented to identify the change type of interest. The dynamic monitoring results for Shanghai, Shenzhen, and Shiyan in China, two megacities with rapid and obvious urbanization and a small city with relatively slow urbanization, indicate that the proposed method achieves a significantly higher accuracy than the current state-of-the art methods. The sample accuracy verified by the high spatial resolution reference maps endorses the applicability of the sample collection, while the reliability-based active learning further ensures the robustness of the proposed method in the label-noise situation. The presented method was tested in two difficult situations (a small training sample case and a training sample set without joint labeling), so that the robustness and accuracy of the approach can be expected to be of a similar or better quality in cases with more training samples. Given its effectiveness and robustness, the proposed method could be widely applied in LULC change dynamic monitoring. [ABSTRACT FROM AUTHOR]

To achieve its carbon peak and neutrality targets, China is in need of balancing environmental and economic sustainability; In addition, whether environmental regulations can gain economic dividends while protecting the environment is particularly important. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021, this study takes China's environmental fee-to-tax as a quasi-natural experiment, and adopts the difference-in-difference-in-differences model to evaluate the impact of environmental tax on enterprises' total factor productivity (ETFP). The results show that the environmental fee-to-tax significantly hinders the growth of ETFP. Further, this negative impact mainly stems from the fact that the reform prompts enterprises to choose strategic innovation and conduct greenwashing behavior. Furthermore, when considering enterprises' heterogeneity, we find that enterprises with large scale, high investment efficiency, state ownership, high market competitiveness and located in western regions are more resistant to policy shocks. This paper provides reliable empirical evidence and useful policy implications for further formulating environmental tax policies to accomplish the "win-win" goal for both the environment and the economy. [ABSTRACT FROM AUTHOR]

Stimulating enterprises to implement environmental management system certification (EMSC, that is to say ISO14001 certification) to the greatest extent and resolving their obstacles in time are the basis for promoting green development of enterprises. Using the full samples of Shanghai and Shenzhen A-share listed industrial enterprises from 2010 to 2019, and employing the Logit model and Propensity Score Matching method (PSM), this paper empirically analyzes the motivations and obstacles for Chinese enterprises to implement EMSC certification, further explores the transformation between advantages and disadvantages under certain conditions, and the differentiation characteristics among enterprises under different social responsibility, property rights and enterprise size. The results show that: ① There is a "U"-shaped relationship between corporate social responsibility and EMSC, and the social responsibility of non-state-owned enterprises could realize the transformation from obstacle to motivation earlier than that of state-owned enterprises. ② There is an inverted "U"-shaped relationship between environmental regulation and EMSC, precisely speaking, this inverted "U"-shaped relationship only exists in non-state-owned enterprises, large-scale enterprises and enterprises with high social responsibility. ③ The public environmental awareness could promote enterprises to implement EMSC, and would strengthen the impact of environmental regulations on enterprises' implementation of EMSC. ④ Based on Propensity Score Matching method, it is concluded that Chinese enterprises indeed improve their environmental performance rather than their financial performance by EMSC. The conclusions provide helpful practical guidance for comprehensively revealing the motivations and obstacles for China's enterprises to implement EMSC. [ABSTRACT FROM AUTHOR]

Intelligent forecasting model of stock price is an effective way to obtain ideal investment returns. Due to the impact of quantitative transactions, traditional forecasting methods face challenges and pressures. How to find a reliable forecasting model and improve the forecasting accuracy will be a scientific problem worthy of further discussion. This paper proposes an intelligent forecasting model of stock price based on neighborhood rough set (NRS) and multivariate empirical mode decomposition (MEMD) by means of the ideal of "granular computing" and "decomposition ensemble". Firstly, the multiscale permutation entropy (MPE) method is conducted to determine decision features, which can improve the stability of permutation entropy. Then, a new feature selection method, fusing mutual information (MI) and the NRS, is proposed to automatically capture and select the condition features from the stock market. Subsequently, aiming on revealing the more detailed feature information and maintaining relevance between features, the MEMD is utilized to simultaneously decompose all features. Finally, the decomposed features are inputted into the long short-term memory (LSTM) network to train the intelligent forecasting model and provide the forecasting results of stock price. The validity of our proposed model is assessed through the stocks of Shanghai and Shenzhen markets. The results show that our proposed intelligent forecasting model of stock price makes a beneficial attempt and discussion for the integration of "granular computing" and "decomposition ensemble". And it will provide scientific support and reference for investors' actual investment decisions. [ABSTRACT FROM AUTHOR]

• Introduces a hybrid forecasting framework to identify enterprise financing risk. • Develops a feature selection module to identify the optimal input variables. • Proposes an improved ELM prediction model for enterprise financing risk. • Demonstrates the framework's reliability in forecasting enterprise financing risk. A scientific framework that can effectively forecast enterprise financing risks can both promote enterprise management and reduce the cost of risk for financial institutions. This study constructs a novel hybrid forecasting framework for enterprise financing risk incorporating modules for data preprocessing, feature selection, forecasting, and evaluation. Specifically, the data preprocessing module mainly realizes the prescreen financing risk indicators and solves the forecasting challenge created by imbalanced data; The feature selection module based on binary grey wolf optimization is designed to intelligently identify optimal financing risk indicators; The forecasting module based on the improved extreme learning machine model established in this paper achieves higher forecasting accuracy; and the evaluation module provides reasonable and scientific evaluations of the proposed hybrid forecasting framework by using the data from small and medium-sized enterprises (SMEs) in China and all listed enterprises with Shanghai and Shenzhen A-shares. Using the SMEs dataset as an example, the Type-2 error value of the developed hybrid forecasting framework is 0.1765, which is 70.24% lower than the average result of the other models; the G-mean value of the framework is 0.8566, which is 40.56% higher than the average result of the other models. Based on the results, the proposed hybrid forecasting framework outperforms other comparative models and is a reliable tool for forecasting enterprise financing risk. [ABSTRACT FROM AUTHOR]

This article contributes to the recent literature in the spatial diffusion pattern of house prices by constructing spatial-temporal model to test the spatial relationships of 70 housing markets in China. Drawing on the spatial-temporal method proposed by Holly, a multiregional spatial-temporal model suitable for China is formulated which are applied to study the spatial-temporal diffusion effect of urban housing prices in China. Firstly, the diffusion mechanism of urban housing prices is analyzed from theoretical perspective, and four cities are assumed as dominant cities, namely, Beijing, Shanghai, Shenzhen and Sanya. Secondly, the spatial linkage of the housing prices of 70 large and medium-sized cities from 2007 to 2017 is examined by calculating the global Moran's I index. Thirdly, a spatial-temporal model is constructed to empirically analyze the diffusion effects of the housing prices of 70 sample cites. The study concludes that 1) significant global spatial autocorrelation is observed among the housing prices in the 70 cites; 2) empirical analysis shows that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities of the 70 cities; and 3) the spatial effects of the four dominant cities on other cities differ in areas, degrees and duration. • This paper takes 70 large and medium-sized cities as research sample to study the temporal and spatial diffusion mechanisms of housing prices, analyzes the fluctuation rules of regional housing prices and provides a reference for the governments to implement differentiated regulation. • This study makes several major contributions: (1) We use the global Moran's I to verify whether housing prices in 70 large and medium-sized cities from 2007 to 2017 are spatially linked. (2) Based on the theory of "regional growth poles" and "ripple effect," we portray the spatial and temporal diffusion mechanism of regional housing prices and verify the existence of four dominant cities in China, they are Beijing, Shanghai, Shenzhen and Sanya. (3) Taking Beijing, Shanghai, Shenzhen and Sanya as the dominant cities, we construct a temporal and spatial diffusion model, validate the theoretical hypothesis, analyze and compare the diffusion characteristics of housing price in different dominant cities using spatial and temporal impulse response functions. • The main findings of this paper include: (1) Based on spatial autocorrelation analysis, there is a significant global spatial autocorrelation between housing prices in 70 large and medium cities and there is a spatial linkage between different cities. (2) The estimated results of the spatial and temporal diffusion model show that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities in China, which has verified the theoretical hypothesis. (3) The diffusion characteristics of the housing prices in these four dominant cities differ in the aspects of area, degree and duration. • This paper can propose the following suggestions for the relevant entities in the real estate market at home and abroad: (1) For governments, focus on the dominant cities of each country, and formulate real estate control policies in a specific way by identifying the differences in the impact of changes in housing prices in each dominant city on itself and other regions in terms of area, degree and duration. Furthermore, manage the real estate market by combining the actual conditions of each country or region, such as population mobility, economic development level, and the degree of marketization. (2) For investor and housing demanders, according to the similarities and differences of housing price diffusion mechanisms in various countries, predict the future development trend of housing prices in a certain region, adjust investment portfolios, and make housing consumption decisions scientifically. [ABSTRACT FROM AUTHOR]

Green technology innovation is the principal approach to cleaner production and achieving sustainable development. This paper focuses on the impact of the synergy effect between external government subsidies and internal slack on green technology innovation, as well as the moderating role of ownership concentration in this process. Using China's data of the high-tech enterprises listed in Shanghai and Shenzhen from 2011 to 2016, the hypotheses are tested based on the fixed effect model of negative binomial distribution. The results suggest that: a) the synergy of government subsidies and unabsorbed slack effectively improves the enterprises' green technical innovation, while the synergy of government subsidies and absorbed slack negatively affect the enterprises' green technical innovation; and b) ownership concentration positively moderates the relation between green technology innovation and the synergy of government subsidies and unabsorbed slack with a inverted U-shaped curve, while there is no significance in their absorbed slack counterparts. Moreover, the baseline conclusions are applicable to non-state-owned enterprises, while they differ in state-owned enterprises regarding the property heterogeneity: the synergistic effect between government subsidies and absorbed slack has no significance on the green technology innovation of these state-owned enterprises; and the ownership concentration positively moderates the relation between green technology innovation and the synergy of government subsidies and unabsorbed slack with a U-shaped curve. Thus, an integrated framework of public supported resources, internal resources and the ownership concentration is built to optimize the allocation of external and internal resources for enterprises to develop green new technologies and ultimately promote the sustainability of the environment. This paper focuses on the impact of synergy effect between external government subsidies and internal slack on green technology innovation, as well as the moderating role of ownership concentration in this process. Image 1 • Systematically integrates external subsidies, internal slack resources, ownership concentration and green technology innovation. • The synergy between subsidies and slack resources on green technology innovation is moderated by ownership concentration. • Both absorbed slack and unabsorbed slack are readily recombined with subsidies to promote green technology innovation. • The relationship among subsidies, slack resources, equity and green technology innovation is heterogeneous in property. [ABSTRACT FROM AUTHOR]

• Knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities. • The quality of this output has increased over time Chinese cities improve the quality of their knowledge output before increasing the quality of technology. • Only Beijing, Shanghai & Shenzhen compete globally in terms of quality of output – other Chinese cities do not. • This can be a mechanism for increasing regional inequality within China. Are Chinese cities becoming smarter in terms of the knowledge and technologies they produce? For decades, the widely held believe in the global North is that China merely copies, imitates and only incrementally improves ideas. Recently, this believe is increasingly being challenged. In this paper I quantify this process. Using data from about 6,1 million patents and 60 million academic publications I examine the complexity of technologies and knowledge developed in major Chinese cities and compare these to those produced in other global cities. The results show that (1) knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities; (2) the quality of this output has increased over time; (3) Chinese cities improve the quality of their knowledge output before increasing the quality of technology; and (4) only Beijing, Shanghai and Shenzhen compete with other global cities in terms of the quality of output, suggesting emerging regional inequality within China linked to knowledge and technology production. Not only is the size of the Chinese economy increasing, so is the quantity and quality of its knowledge and technology output. [ABSTRACT FROM AUTHOR]

This paper examines the daily return series of five main indexes, including Shanghai Stock Exchange Composite Index(SSE), Shenzhen Stock Exchange Component Index(SZSE), Shanghai Shenzhen 300 Index(SHSE-SZSE300), Small and Medium Enterprise Board Index(SME), and ChiNext Index(ChiNext), in different boards of Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The return series exhibit significant multifractal properties on the whole time scale and ChiNext has the lowest multifractal properties among the five indexes, indicating the highest market efficiency. The multifractal properties of the five indexes are due to both long-range correlation and fat-tail characteristics of non-Gaussian probability density function, and these two factors have different effects on the multifractality of five indexes. The crosscorrelations among different boards of Chinese stock market reflect the internal linkages between different boards. This paper also aims to compare the multifractality degrees of Main-Board, Small and Medium Enterprise Board(SME Board), and Growth Enterprises Market Board(GEM Board) in three sub-samples divided by the 2015 stock market crash and to study its effects on efficiency and risk of these boards in China's stock market in each sub-sample, from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China's stock market. The findings of the study may also provide important implications for further study on the dynamic mechanism and efficiency in stock market and help regulators and policymakers effectively control the market risk and achieve more effective resource allocation. • Returns in different boards of Chinese stock market exhibit significant nonlinear multifractal properties. • The whole sample from 2000 to 2018 is divided into three sub-samples according to the 2015 stock market crash. • The efficiencies of Chinese stock market experienced volatilities in sub-samples. • The efficiencies of Chinese stock market are affected differently by the 2015 stock market crash. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes the multifractality in Shanghai and Shenzhen stock markets using multifractal spectrum analysis and multifractal detrended fluctuation analysis. We find that the main source of multifractality is long-range correlations of large and small fluctuations. Then, we introduce a multifractal volatility measure (MV) and find that by taking MV as daily conditional volatility, the simulated series displayed similar “stylized facts” to the original daily return series. By capturing the dynamics of MV using the ARFIMA model, we find that the out-of-sample forecasting performance of the ARFIMA-MV model is better than some GARCH-class models and the ARFIMA-RV model under some criteria of loss function. [Copyright &y& Elsevier]

Carbon prices are low and fluctuate greatly since Chinese carbon emissions trading scheme pilots were operated. This paper discusses the price drivers in the pilots, using structural breaks test and autoregressive distributed lag model. The results indicate that oversupply of allowances, low auction prices and use of China certified emission reductions will cause remarkable decline on carbon prices; the expansion of carbon market and centralized trading will rise carbon prices. Oil prices are positively correlated with carbon prices, and coal prices are negatively related with carbon prices. Carbon-intensive product prices also affect carbon prices. Chemical prices are positively linked with carbon prices in Shenzhen, while negatively related with those in Beijing and Shanghai, for differences in sectoral coverage. Nonferrous prices have positive impacts on carbon prices in Beijing but negative impacts on those in Shenzhen, for differences in industrial structure. Financial markets and weather have limited impacts on carbon prices. Our results provide policy implications for the development of pilots and the national carbon market. [ABSTRACT FROM AUTHOR]